scholarly journals Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

2021 ◽  
Vol 13 (12) ◽  
pp. 6689
Author(s):  
Lara Waltersmann ◽  
Steffen Kiemel ◽  
Julian Stuhlsatz ◽  
Alexander Sauer ◽  
Robert Miehe
Keyword(s):  
Artificial Intelligence ◽  
Resource Efficiency ◽  
Ecological Crisis ◽  
Manufacturing Companies ◽  
Potential Influence ◽  
Business Units ◽  
Predictive Quality ◽  
Ecological Performance ◽  
Use Phase ◽  
Meta Analyses

Sustainability improvements in industrial production are essential for tackling climate change and the resulting ecological crisis. In this context, resource efficiency can directly lead to significant advancements in the ecological performance of manufacturing companies. The application of Artificial Intelligence (AI) also plays an increasingly important role. However, the potential influence of AI applications on resource efficiency has not been investigated. Against this background, this article provides an overview of thecurrent AI applications and how they affect resource efficiency. In line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this paper identifies, categorizes, and analyzes seventy papers with a focus on AI tasks, AI methods, business units, and their influence on resource efficiency. Only a minority of papers was found to address resource efficiency as an explicit objective. Subsequently, typical use cases of the identified AI applications are described with a focus on predictive maintenance, production planning, fault detection and predictive quality, as well as the increase in energy efficiency. In general, more research is needed that explicitly considers sustainability in the development and use phase of AI solutions, including Green AI. This paper contributes to research in this field by systematically examining papers and revealing research deficits. Additionally, practitioners are offered the first indications of AI applications increasing resource efficiency.

scholarly journals Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2146
Author(s):  
Manuel Andrés Vélez-Guerrero ◽  
Mauro Callejas-Cuervo ◽  
Stefano Mazzoleni
Keyword(s):  
Artificial Intelligence ◽  
Upper Limb ◽  
Main Trend ◽  
Motor Rehabilitation ◽  
Rehabilitation Process ◽  
Upper Limb Rehabilitation ◽  
Multiple Sensors ◽  
Meta Analyses ◽  
Limb Rehabilitation ◽  
And Control

Processing and control systems based on artificial intelligence (AI) have progressively improved mobile robotic exoskeletons used in upper-limb motor rehabilitation. This systematic review presents the advances and trends of those technologies. A literature search was performed in Scopus, IEEE Xplore, Web of Science, and PubMed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology with three main inclusion criteria: (a) motor or neuromotor rehabilitation for upper limbs, (b) mobile robotic exoskeletons, and (c) AI. The period under investigation spanned from 2016 to 2020, resulting in 30 articles that met the criteria. The literature showed the use of artificial neural networks (40%), adaptive algorithms (20%), and other mixed AI techniques (40%). Additionally, it was found that in only 16% of the articles, developments focused on neuromotor rehabilitation. The main trend in the research is the development of wearable robotic exoskeletons (53%) and the fusion of data collected from multiple sensors that enrich the training of intelligent algorithms. There is a latent need to develop more reliable systems through clinical validation and improvement of technical characteristics, such as weight/dimensions of devices, in order to have positive impacts on the rehabilitation process and improve the interactions among patients, teams of health professionals, and technology.

scholarly journals Augmented Reality, Virtual Reality and Artificial Intelligence in Orthopedic Surgery: A Systematic Review

2021 ◽  
Vol 11 (7) ◽  
pp. 3253
Author(s):  
Umile Giuseppe Longo ◽  
Sergio De Salvatore ◽  
Vincenzo Candela ◽  
Giuliano Zollo ◽  
Giovanni Calabrese ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Virtual Reality ◽  
Augmented Reality ◽  
Orthopaedic Surgery ◽  
Resident Training ◽  
Comprehensive Search ◽  
Virtual And Augmented Reality ◽  
Orthopaedic Training ◽  
Meta Analyses

Background: The application of virtual and augmented reality technologies to orthopaedic surgery training and practice aims to increase the safety and accuracy of procedures and reducing complications and costs. The purpose of this systematic review is to summarise the present literature on this topic while providing a detailed analysis of current flaws and benefits. Methods: A comprehensive search on the PubMed, Cochrane, CINAHL, and Embase database was conducted from inception to February 2021. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines were used to improve the reporting of the review. The Cochrane Risk of Bias Tool and the Methodological Index for Non-Randomized Studies (MINORS) was used to assess the quality and potential bias of the included randomized and non-randomized control trials, respectively. Results: Virtual reality has been proven revolutionary for both resident training and preoperative planning. Thanks to augmented reality, orthopaedic surgeons could carry out procedures faster and more accurately, improving overall safety. Artificial intelligence (AI) is a promising technology with limitless potential, but, nowadays, its use in orthopaedic surgery is limited to preoperative diagnosis. Conclusions: Extended reality technologies have the potential to reform orthopaedic training and practice, providing an opportunity for unidirectional growth towards a patient-centred approach.

Automatic information retrievement for exporting services: First project findings from the development of an AI based export decision supporting instrument

2021 ◽  
pp. 2-11
Author(s):  
David Aufreiter ◽  
Doris Ehrlinger ◽  
Christian Stadlmann ◽  
Margarethe Uberwimmer ◽  
Anna Biedersberger ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Research Process ◽  
Training Data ◽  
Future Research ◽  
Manufacturing Companies ◽  
Market Information ◽  
Annotation Scheme ◽  
Export Decisions ◽  
Decision Supporting

On the servitization journey, manufacturing companies complement their offerings with new industrial and knowledge-based services, which causes challenges of uncertainty and risk. In addition to the required adjustment of internal factors, the international selling of services is a major challenge. This paper presents the initial results of an international research project aimed at assisting advanced manufacturers in making decisions about exporting their service offerings to foreign markets. In the frame of this project, a tool is developed to support managers in their service export decisions through the automated generation of market information based on Natural Language Processing and Machine Learning. The paper presents a roadmap for progressing towards an Artificial Intelligence-based market information solution. It describes the research process steps of analyzing problem statements of relevant industry partners, selecting target countries and markets, defining parameters for the scope of the tool, classifying different service offerings and their components into categories and developing annotation scheme for generating reliable and focused training data for the Artificial Intelligence solution. This paper demonstrates good practices in essential steps and highlights common pitfalls to avoid for researcher and managers working on future research projects supported by Artificial Intelligence. In the end, the paper aims at contributing to support and motivate researcher and manager to discover AI application and research opportunities within the servitization field.

scholarly journals KEMAMPUAN PERGANTIAN AUDITOR DAN PRIOR OPINION MEMODERASI PENGARUH POTENSI FINANCIAL DISTRESS TERHADAP OPINI GOING CONCERN

2017 ◽  
pp. 3259
Author(s):  
Putu Yudha Asteria Putri ◽  
Ida Bagus Putra Astika ◽  
Made Gede Wirakusuma
Keyword(s):  
Empirical Evidence ◽  
Financial Distress ◽  
Stock Exchange ◽  
Secondary Data ◽  
Manufacturing Companies ◽  
Potential Influence ◽  
Going Concern ◽  
Going Concern Opinion ◽  
Prior Opinion ◽  
Contingency Approach

This study aimed to get empirical evidence the auditor's ability to change and prior opinion in moderating influence on the potential financial distress Award going concern opinion. There’s a going concern opinion because the company indicated no longer able to carry out its life. The results of previous studies get inconsistent results in terms of the potential influence on the provision of financial distress going concern opinion. The existence of a contingency approach can be completed in this study, where the variables change of auditor and prior opinion allegedly moderating influence on the potential financial distress Award going concern opinion. This study uses secondary data. Manufacturing companies listed in Indonesia Stock Exchange period 2009-2015 the population in this study by the amount total of the samples are 77 samples were selected by purposive sampling.

scholarly journals Artificial Intelligence for Caregivers of Persons With Alzheimer’s Disease and Related Dementias: Systematic Literature Review (Preprint)

2020 ◽  
Author(s):  
Bo Xie ◽  
Cui Tao ◽  
Juan Li ◽  
Robin C Hilsabeck ◽  
Alyssa Aguirre
Keyword(s):  
Artificial Intelligence ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Digital Library ◽  
Full Text ◽  
Interdisciplinary Collaboration ◽  
Exclusion Criteria ◽  
Cross Sectional ◽  
Meta Analyses ◽  
The Impact

BACKGROUND Artificial intelligence (AI) has great potential for improving the care of persons with Alzheimer’s disease and related dementias (ADRD) and the quality of life of their family caregivers. To date, however, systematic review of the literature on the impact of AI on ADRD management has been lacking. OBJECTIVE This paper aims to (1) identify and examine literature on AI that provides information to facilitate ADRD management by caregivers of individuals diagnosed with ADRD and (2) identify gaps in the literature that suggest future directions for research. METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for conducting systematic literature reviews, during August and September 2019, we performed 3 rounds of selection. First, we searched predetermined keywords in PubMed, Cumulative Index to Nursing and Allied Health Literature Plus with Full Text, PsycINFO, IEEE Xplore Digital Library, and the ACM Digital Library. This step generated 113 nonduplicate results. Next, we screened the titles and abstracts of the 113 papers according to inclusion and exclusion criteria, after which 52 papers were excluded and 61 remained. Finally, we screened the full text of the remaining papers to ensure that they met the inclusion or exclusion criteria; 31 papers were excluded, leaving a final sample of 30 papers for analysis. RESULTS Of the 30 papers, 20 reported studies that focused on using AI to assist in activities of daily living. A limited number of specific daily activities were targeted. The studies’ aims suggested three major purposes: (1) to test the feasibility, usability, or perceptions of prototype AI technology; (2) to generate preliminary data on the technology’s performance (primarily accuracy in detecting target events, such as falls); and (3) to understand user needs and preferences for the design and functionality of to-be-developed technology. The majority of the studies were qualitative, with interviews, focus groups, and observation being their most common methods. Cross-sectional surveys were also common, but with small convenience samples. Sample sizes ranged from 6 to 106, with the vast majority on the low end. The majority of the studies were descriptive, exploratory, and lacking theoretical guidance. Many studies reported positive outcomes in favor of their AI technology’s feasibility and satisfaction; some studies reported mixed results on these measures. Performance of the technology varied widely across tasks. CONCLUSIONS These findings call for more systematic designs and evaluations of the feasibility and efficacy of AI-based interventions for caregivers of people with ADRD. These gaps in the research would be best addressed through interdisciplinary collaboration, incorporating complementary expertise from the health sciences and computer science/engineering–related fields.

scholarly journals Artificial Intelligence-Aid Colonoscopy Vs. Conventional Colonoscopy for Polyp and Adenoma Detection: A Systematic Review of 7 Discordant Meta-Analyses

2022 ◽  
Vol 8 ◽  
Author(s):  
Hui Pan ◽  
Mingyan Cai ◽  
Qi Liao ◽  
Yong Jiang ◽  
Yige Liu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Systematic Reviews ◽  
Detection Rate ◽  
Conventional Colonoscopy ◽  
Cochrane Library ◽  
High Quality ◽  
Decision Algorithm ◽  
Adenoma Detection ◽  
Meta Analyses

Objectives: Multiple meta-analyses which investigated the comparative efficacy and safety of artificial intelligence (AI)-aid colonoscopy (AIC) vs. conventional colonoscopy (CC) in the detection of polyp and adenoma have been published. However, a definitive conclusion has not yet been generated. This systematic review selected from discordant meta-analyses to draw a definitive conclusion about whether AIC is better than CC for the detection of polyp and adenoma.Methods: We comprehensively searched potentially eligible literature in PubMed, Embase, Cochrane library, and China National Knowledgement Infrastructure (CNKI) databases from their inceptions until to April 2021. Assessment of Multiple Systematic Reviews (AMSTAR) instrument was used to assess the methodological quality. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist was used to assess the reporting quality. Two investigators independently used the Jadad decision algorithm to select high-quality meta-analyses which summarized the best available evidence.Results: Seven meta-analyses met our selection criteria finally. AMSTAR score ranged from 8 to 10, and PRISMA score ranged from 23 to 26. According to the Jadad decision algorithm, two high-quality meta-analyses were selected. These two meta-analyses suggested that AIC was superior to CC for colonoscopy outcomes, especially for polyp detection rate (PDR) and adenoma detection rate (ADR).Conclusion: Based on the best available evidence, we conclude that AIC should be preferentially selected for the route screening of colorectal lesions because it has potential value of increasing the polyp and adenoma detection. However, the continued improvement of AIC in differentiating the shape and pathology of colorectal lesions is needed.

Ecological Performance as a New Metric to Measure Green Supply Chain Practices

2019 ◽  
pp. 1003-1014
Author(s):  
June Poh Kim Tam ◽  
Yudi Fernando
Keyword(s):  
Energy Use ◽  
Sustainable Manufacturing ◽  
Market Price ◽  
Cost Effective ◽  
Value Stream Mapping ◽  
Manufacturing Processes ◽  
Competitive Advantages ◽  
Manufacturing Companies ◽  
Value Stream ◽  
Ecological Performance

This chapter discusses the concept of ecological performance. Sustainable development has become a focus of governments, companies, communities, and even individuals. Manufacturing companies need a performance measurement that can balance between profit and environment. The concept of reduction of resource use and waste generation is not limited to the manufacturing processes of a focal company, but it can be embraced into product design that benefits the end users as well as stakeholders. Companies can build competitive advantages by integrating ecological performance into business value stream mapping. Reduction of resources and energy use can help companies to be cost effective and thus able to survive a market price war. Companies can enhance product quality through eco-design and sustainable manufacturing to preempt competitors. Companies can build green images to gain customer loyalty. Waste reduction can help companies to save capacity and be more flexible in shipment delivery and support customer dynamic needs.

scholarly journals An investigation into the impact and implications of published papers from retracted research: systematic search of affected literature

BMJ Open ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. e031909 ◽  
Author(s):  
Alison Avenell ◽  
Fiona Stewart ◽  
Andrew Grey ◽  
Greg Gamble ◽  
Mark Bolland
Keyword(s):  
Systematic Review ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Research Misconduct ◽  
Evidence Base ◽  
Bibliographic Databases ◽  
Potential Influence ◽  
Relevant Research ◽  
Meta Analyses ◽  
The Impact

ObjectiveAnalyses of the impact of a body of clinical trial reports subject to research misconduct have been few. Our objective was to examine the impact on clinically relevant research of a group of researchers’ trial reports (‘affected trial reports’) affected by research misconduct, and whether identification of misconduct invoked a reappraisal.DesignIn 2016, we used five databases and search engines to identify ‘citing publications’, that is, guidelines, systematic and other reviews, and clinical trials citing any of 12 affected trial reports, published 1998–2011, eventually retracted for research misconduct. The affected trial reports were assessed more likely to have had impact because they had hip fracture outcomes and were in journals with impact factor >4. Two authors assessed whether findings of the citing publications would change if the affected trial reports were removed. In 2018, we searched for evidence that the citing publications had undertaken a reassessment as a result of the potential influence of the affected trial reports.ResultsBy 2016 the affected trial reports were cited in 1158 publications, including 68 systematic reviews, meta-analyses, narrative reviews, guidelines and clinical trials. We judged that 13 guidelines, systematic or other reviews would likely change their findings if the affected trial reports were removed, and in another eight it was unclear if findings would change. By 2018, only one of the 68 citing publications, a systematic review, appeared to have undertaken a reassessment, which led to a correction.ConclusionsWe found evidence that this group of affected trial reports distorted the evidence base. Correction of these distortions is slow, uncoordinated and inconsistent. Unless there is a rapid, systematic, coordinated approach by bibliographic databases, authors, journals and publishers to mitigate the impact of known cases of research misconduct, patients, other researchers and their funders may continue to be adversely affected.

Contribution for the Life Cycle Oriented Evaluation of Costs and Resource Efficiency of Production Machines in Procurement

2014 ◽  
Vol 655 ◽  
pp. 9-14
Author(s):  
Vanessa Schmidt ◽  
Markus Graßl ◽  
Gunther Reinhart
Keyword(s):  
Life Cycle ◽  
Environmental Impact ◽  
Resource Efficiency ◽  
Use Phase ◽  
Factory Site

Even though the share of the production machine’s environmental impact might be negligible compared to the product’s own use phase, the environmental impact at the factory site of is potentially significant. Thus, this article presents a contribution for the life cycle oriented evaluation of costs and resource efficiency applied in procurement of a production machine.

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